首页> 外文会议>International Conference on Recent Advances and Innovations in Engineering >Reliability Constrained Day Ahead Unit Commitment with Optimal Spinning Reserve Allocation for Solar Integrated Power System
【24h】

Reliability Constrained Day Ahead Unit Commitment with Optimal Spinning Reserve Allocation for Solar Integrated Power System

机译:可靠性约束日联盟与太阳能集成电力系统最优纺纱储备分配的承诺

获取原文

摘要

Unit commitment is becoming a complex problem with the increasing constraints due to the restructuring of power system and the escalation in inclusion of various types of Distributed Generation sources. These sources offer a significantly lower generation compared to the conventional sources. Also they pose synchronising problems with the grid since the electricity cannot be transmitted over long distances and thus they provide the localized consumption of energy. In this paper, unit commitment is performed with optimal spinning reserve allocation and the assessment of reliability in terms of loss of load, in the presence of solar integration into the power system. The ‘Loss Of Load Probability’ (LOLP) index is utilized for determining the level of reliability of the obtained results. The Spinning Reserve (SR) considered in the UC calculations, is a constant value and it is not varied with respect to the changes in solar generation. Here, the spinning reserve optimality is determined with respect to the changes in power injection due to the solar energy integration into the power system. Dynamic programming technique is applied on two systems (four generator and ten generator systems) and the results are compared with those obtained without the consideration of LOLP, SR optimality and solar energy sources.
机译:由于电力系统的重组和包括各种类型的分布生成来源的升级,单位承诺正在成为一个复杂的问题。与传统来源相比,这些来源具有显着较低的发电。此外,它们弥合了网格的同步问题,因为电力不能长距离传输,因此它们提供了局部消耗的能量。在本文中,在太阳集成到电力系统的情况下,利用最佳旋转储备分配和对负载损失方面的可靠性进行评估来进行单位承诺。 “负载概率损失”(LOLP)索引用于确定所获得的结果的可靠性水平。在UC计算中考虑的纺纱储备(SR)是恒定值,并且对于太阳能发生的变化而言,不变。这里,由于太阳能集成到电力系统,相对于电力注入的变化确定纺丝储备最优。动态编程技术应用于两个系统(四个发生器和十个发电机系统),并将结果与​​未考虑的LOLP,SR最优能源和太阳能源进行比较。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号